Parameterisation of slant-Haar transforms

A parameterisation of the slant-Haar transform is presented, which includes an existing version of the slant-Haar transform. An efficient algorithm for the slant-Haar transform is developed and its computational complexity is estimated. The parametric slant-Haar transforms are compared to the Karhunen-Loeve transform. The parametric slant-Haar is shown to perform better than the commonly used slant-Haar and slant-Hadamard transforms for the first-order Markov model and also performs better than the discrete cosine transform for images approximated by the generalised image correlation model     

Implementation of Digital Electronic Arithmetics and its application in image processing

In this paper we introduce new algorithm implementations of a new parametric image processing framework that will accurately process images and speed up computation for addition, subtraction, and multiplication. Its potential applications include computer graphics, digital signal processing and other multimedia applications. This Parameterized Digital Electronic Arithmetic (PDEA) model replaces linear operations with non-linear ones. The implementation of a parameterized model is presented. We also present the design of arithmetic circuits including parallel counters, adders and multipliers based in two high performance threshold logic gate implementations that we have developed. We will also explore new microprocessor architectures to take advantage of arithmetic. The experiments executed have shown that the algorithm provides faster and better enhancements from those described in the literature. The FPGA chips used is Spartan 3E from Xilinix. The critical length in the circuit implemented on the FPGA had the minimum period for the proposed subsystem is 10.209 ns (maximum frequency 97.957 MHz). Maximum power consumed is 2.4 mW using 32 nm process and we used parallelism and reuse of the Hardware components to accomplish and speed up the process.     

Impulse Noise Detector Performance Measure Based on Intensity Volume

An accurate detector performance evaluation method provides a fair comparison platform and can also support in parameter optimization for existing Impulse noise detectors in the applications of medical imaging. The Impulse noise detector performance measure (INDPM) package is widely applied as tools for quantitative comparison among detectors, which contains recall measure, accuracy measure, precision measure, specificity measure and F-measure. However, these five measures suffer from limited accuracy in correctly evaluating the performance of a detector and are not in well agreement with human subjective evaluation. To solve this problem, five new measures are proposed by introducing a new concept of intensity volume to form a new Impulse noise detector performance package (IV-INDPM). Using a standard image dataset, we conduct experimental and comparative tests with 32 different original images and 5 different existing detectors. Results demonstrate the superior performance of each new measure within IV-INDPM in reaching a much closer agreement with human subjective evaluation, compared to existing measures in INDPM. Even though five new measures are efficient in evaluating detectors’ performance from different perspectives, a new benchmark algorithm (IND-BA) is proposed as a robust and overall metric for ease of general-purpose use by making the most of these five new measures. Comparison results demonstrate its efficiency and accuracy.     

Transform coefficient histogram-based image enhancement algorithms using contrast entropy.

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.     

Human visual system-based image enhancement and logarithmic contrast measure.

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.     

Decomposition of Binary Matrices and Fast Hadamard Transforms

Binary matrices or (± 1)-matrices have numerous applications in coding, signal processing, and communications. In this paper, a general and efficient algorithm for decomposition of binary matrices and the corresponding fast transform is developed. As a special case, Hadamard matrices are considered. The difficulties of the construction of 4n-point Hadamard transforms are related to the Hadamard problem: the question of the existence of Hadamard matrices. (It is not known whether for every integer n, there is an orthogonal 4n × 4n matrix with elements ± 1.) In the derived fast algorithms, the number of real operations is reduced from O(N²) to O(N log N) compared to direct computation. The proposed scheme requires no zero padding of the input data. Comparisions revealing the efficiency of the proposed algorithms with respect to the known ones are given. In particular, it is demonstrated that, in typical applications, the proposed algorithm is significantly more efficient than the conventional Walsh-Hadamard transform. Note that for Hadamard matrices of orders ≥ 96 the general algorithm is more efficient than the classical Walsh-Hadamard transform whose order is a power of 2. The algorithm has a simple and symmetric structure. The results of numerical examples are presented.     

An adaptive text-line extraction algorithm for printed Arabic documents with diacritics

Multimedia Tools and Applications - The performance of document text recognition depends on text line segmentation algorithms, which heavily relies on the type of language, author’s writing...     

Transform-based image enhancement algorithms with performancemeasure

This paper presents a new class of the "frequency domain"-based signal/image enhancement algorithms including magnitude reduction, log-magnitude reduction, iterative magnitude and a log-reduction zonal magnitude technique. These algorithms are described and applied for detection and visualization of objects within an image. The new technique is based on the so-called sequency ordered orthogonal transforms, which include the well-known Fourier, Hartley, cosine, and Hadamard transforms, as well as new enhancement parametric operators. A wide range of image characteristics can be obtained from a single transform, by varying the parameters of the operators. We also introduce a quantifying method to measure signal/image enhancement called EME. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.